Graph-Based Memory Recall Recurrent Neural Network for Mid-Term Sea-Surface Height Anomaly Forecasting
文献类型：期刊论文
| 作者 | Zhou, Yuan2; Ren, Tian2; Chen, Keran2; Gao, Le1; Li, Xiaofeng1 |
| 刊名 | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
 |
| 出版日期 | 2024 |
| 卷号 | 17页码:6642-6657 |
| 关键词 | Forecasting Predictive models Atmospheric waves Spatiotemporal phenomena Sea surface Ocean waves Data models Sea-surface height anomaly (SSHA) deep learning (DL) spatiotemporal prediction Rossby waves |
| ISSN号 | 1939-1404 |
| DOI | 10.1109/JSTARS.2024.3368766 |
| 通讯作者 | Gao, Le(gaole@qdio.ac.cn) ; Li, Xiaofeng(xiaofeng.li@ieee.org) |
| 英文摘要 | Sea surface height anomaly (SSHA) plays a pivotal role in ocean dynamics and climate systems. This article develops a graph-based memory recall recurrent neural network (GMR-Net) to achieve accurate and reliable mid-term spatiotemporal prediction of the SSHA field. The proposed method designs a newly developed long-term memory recall cell as the building block of the network, which utilizes the proposed memory store recall (MSR) module to learn and capture the mid- and long-term temporal dependencies of the SSHA field. The MSR module can efficiently recall memories stored in the memory bank across multiple timestamps through the proposed graph representation mechanism even after long periods of disturbance. The mid-term SSHA forecasting is performed with a 30-day ahead, and our proposed GMR-Net model achieves high prediction accuracy in different geographical regions: the Tropical Western Pacific and the South China Sea, yielding an RMSE of 0.026 and 0.035 m, respectively. Compared with advanced prediction models, our proposed GMR-Net model exhibits high reliability and superior performance in mid-term SSHA forecasting. Moreover, marine phenomena, such as Rossby waves, which can cause dramatic changes in sea-surface height, are successfully observed from our forecast data, further verifying the effectiveness of our prediction method. |
| WOS关键词 | EMPIRICAL MODE DECOMPOSITION ; ROSSBY WAVES ; LEVEL RISE ; LARGE-SCALE ; CHINA SEA ; VARIABILITY ; PREDICTION ; ATMOSPHERE ; FREQUENCY ; ALTIMETRY |
| 资助项目 | National Natural Science Foundation of China |
| WOS研究方向 | Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001188473800014 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://ir.qdio.ac.cn/handle/337002/185100]  |
| 专题 | 海洋研究所_海洋环流与波动重点实验室 |
| 通讯作者 | Gao, Le; Li, Xiaofeng |
| 作者单位 | 1.Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Circulat & Waves, Qingdao 266071, Peoples R China 2.Tianjin Univ, Sch Elect & Informat Engn, Tianjin 300072, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhou, Yuan,Ren, Tian,Chen, Keran,et al. Graph-Based Memory Recall Recurrent Neural Network for Mid-Term Sea-Surface Height Anomaly Forecasting[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2024,17:6642-6657. |
| APA | Zhou, Yuan,Ren, Tian,Chen, Keran,Gao, Le,&Li, Xiaofeng.(2024).Graph-Based Memory Recall Recurrent Neural Network for Mid-Term Sea-Surface Height Anomaly Forecasting.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,17,6642-6657. |
| MLA | Zhou, Yuan,et al."Graph-Based Memory Recall Recurrent Neural Network for Mid-Term Sea-Surface Height Anomaly Forecasting".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 17(2024):6642-6657. |
入库方式： OAI收割
来源：海洋研究所
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